| | |
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| | | angles.append(preangles) |
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| | | |
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| | | return pre_yaw, pre_pitch, pre_roll, angles, sr_output |
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| | | |
|---|
| | | class Hopenet_new(nn.Module): |
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| | | # This is just Hopenet with 3 output layers for yaw, pitch and roll. |
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| | | def __init__(self, block, layers, num_bins): |
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| | | self.inplanes = 64 |
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| | | super(Hopenet_new, self).__init__() |
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| | | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
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| | | bias=False) |
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| | | self.bn1 = nn.BatchNorm2d(64) |
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| | | self.relu = nn.ReLU(inplace=True) |
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| | | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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| | | self.layer1 = self._make_layer(block, 64, layers[0]) |
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| | | self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
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| | | self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
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| | | self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
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| | | self.avgpool = nn.AvgPool2d(7) |
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| | | self.fc_yaw = nn.Linear(512 * block.expansion, num_bins) |
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| | | self.fc_pitch = nn.Linear(512 * block.expansion, num_bins) |
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| | | self.fc_roll = nn.Linear(512 * block.expansion, num_bins) |
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| | | |
|---|
| | | self.softmax = nn.Softmax() |
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| | | self.fc_finetune_new = nn.Linear(512 * block.expansion + 256 * block.expansion + 3, 3) |
|---|
| | | self.conv1x1 = nn.Conv2d(1024, 64, kernel_size = 1, stride = 1, bias=False) |
|---|
| | | self.maxpool_interm = nn.MaxPool2d(kernel_size=5, stride=3, padding=1) |
|---|
| | | |
|---|
| | | self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda() |
|---|
| | | |
|---|
| | | for m in self.modules(): |
|---|
| | | if isinstance(m, nn.Conv2d): |
|---|
| | | n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
|---|
| | | m.weight.data.normal_(0, math.sqrt(2. / n)) |
|---|
| | | elif isinstance(m, nn.BatchNorm2d): |
|---|
| | | m.weight.data.fill_(1) |
|---|
| | | m.bias.data.zero_() |
|---|
| | | |
|---|
| | | def _make_layer(self, block, planes, blocks, stride=1): |
|---|
| | | downsample = None |
|---|
| | | if stride != 1 or self.inplanes != planes * block.expansion: |
|---|
| | | downsample = nn.Sequential( |
|---|
| | | nn.Conv2d(self.inplanes, planes * block.expansion, |
|---|
| | | kernel_size=1, stride=stride, bias=False), |
|---|
| | | nn.BatchNorm2d(planes * block.expansion), |
|---|
| | | ) |
|---|
| | | |
|---|
| | | layers = [] |
|---|
| | | layers.append(block(self.inplanes, planes, stride, downsample)) |
|---|
| | | self.inplanes = planes * block.expansion |
|---|
| | | for i in range(1, blocks): |
|---|
| | | layers.append(block(self.inplanes, planes)) |
|---|
| | | |
|---|
| | | return nn.Sequential(*layers) |
|---|
| | | |
|---|
| | | def forward(self, x): |
|---|
| | | x = self.conv1(x) |
|---|
| | | x = self.bn1(x) |
|---|
| | | x = self.relu(x) |
|---|
| | | x = self.maxpool(x) |
|---|
| | | |
|---|
| | | x = self.layer1(x) |
|---|
| | | x = self.layer2(x) |
|---|
| | | x = self.layer3(x) |
|---|
| | | x_interm = self.conv1x1(x) |
|---|
| | | x_interm = self.relu(x_interm) |
|---|
| | | x_interm = self.maxpool_interm(x_interm) |
|---|
| | | x_interm = x_interm.view(x_interm.size(0), -1) |
|---|
| | | |
|---|
| | | x = self.layer4(x) |
|---|
| | | |
|---|
| | | x = self.avgpool(x) |
|---|
| | | x = x.view(x.size(0), -1) |
|---|
| | | pre_yaw = self.fc_yaw(x) |
|---|
| | | pre_pitch = self.fc_pitch(x) |
|---|
| | | pre_roll = self.fc_roll(x) |
|---|
| | | |
|---|
| | | yaw = self.softmax(pre_yaw) |
|---|
| | | yaw = Variable(torch.sum(yaw.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99 |
|---|
| | | pitch = self.softmax(pre_pitch) |
|---|
| | | pitch = Variable(torch.sum(pitch.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99 |
|---|
| | | roll = self.softmax(pre_roll) |
|---|
| | | roll = Variable(torch.sum(roll.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99 |
|---|
| | | yaw = yaw.view(yaw.size(0), 1) |
|---|
| | | pitch = pitch.view(pitch.size(0), 1) |
|---|
| | | roll = roll.view(roll.size(0), 1) |
|---|
| | | preangles = torch.cat([yaw, pitch, roll], 1) |
|---|
| | | |
|---|
| | | # angles predicts the residual |
|---|
| | | residuals = self.fc_finetune_new(torch.cat((preangles, x_interm, x), 1)) |
|---|
| | | final_angles = preangles + residuals |
|---|
| | | |
|---|
| | | return pre_yaw, pre_pitch, pre_roll, preangles, final_angles |
|---|
